SaaS应用交付平台中多租户云数据管理关键技术研究
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摘要
随着云计算的发展及应用软件的成熟,软件即服务(Software as a Service,SaaS)作为云计算的一种应用形式,越来越受到重视,已逐渐成为中小企业应用先进技术的重要途径。SaaS应用交付平台推动了SaaS模式的蓬勃发展,越来越多的个人或机构通过SaaS平台租赁各种形式的应用,这些应用正在覆盖人们生活的各个领域,通过应用及应用之间的协同工作,完成搜索、事务管理以及分析等。
目前,成熟的SaaS服务提供商多采用一对多的软件交付模式,成千上万租户共享一个应用,业务数据存储在服务提供商的共享数据库中,最终用户感受不到所使用的实例在同一时间也为其他客户所共享。支持租户定制的多租户共享存储架构,实现了从操作系统到数据结构等各个级别的资源共享,充分利用了硬件、数据库等资源,然而单个节点所能支持的租户数量受到硬件限制,当租户需要更多存储空间和更高服务质量时,需要采用升级硬件的方式实现,难以实现规模的动态扩展。随着SaaS交付平台租户数目及信息数据量呈几何曲线性增长,SaaS交付平台数据管理正处于由单数据节点往云中多数据节点转变的必然阶段,而相应地对SaaS应用交付平台数据管理能力的期望值也越来越高。
本文致力于SaaS应用交付平台中多租户云数据管理关键技术的研究,目标在于最大限度地支持快速开发与交付,保障SaaS应用多租户的高效运行。SaaS应用交付平台多租户云数据管理具有自身的特点,现有云数据管理方法不能有效解决以下问题：(1)SaaS平台租户数据弹性扩展问题。SaaS应用交付平台涉及到大量的SaaS应用,各应用业务领域不同,数据模式必然千差万别；每个SaaS应用拥有众多的租户,租户在同一应用下的模式类似,但业务又不尽相同；即便是同一租户在不同的发展阶段,数据模式也会有各自的特点。因此,平台要支持数据存储模型的弹性扩展,同时,还要解决因为数据管理复杂度的增加而导致的性能下降问题,保证应用的高效运转。(2)现有云数据放置策略未引入SaaS特征。面对SaaS交付平台的海量数据,云数据库分配云中的多个数据节点为其提供服务,所有SaaS应用所有租户的数据作为一个整体被分割放置到各个节点上,由于SaaS平台租户共享存储的特点,云数据库无法根据数据模式对其进行虚拟化,租户做为独立个体的特征往往被忽略,从而导致多租户数据的混合放置以及单个租户数据的分散,增加了租户数据访问及应用之间数据共享的代价,因此,需要在云数据管理中引入平台租户数据的特征,才能有效进行SaaS平台数据分割及云数据管理的弹性伸缩。(3)缺少多级别、细粒度的SaaS平台租户索引支持。现有的云数据管理机制针对每个用户建立一个虚拟数据库,意识不到SaaS应用交付平台中的应用租户,无法精确的定位到租户数据节点上；同时,在租户数据共享存储的局部节点上,传统的索引机制已经失效,无法提供有效的租户逻辑索引,使得租户的随机数据操作变的困难。
本文以SaaS应用交付平台中多租户云数据管理为目标,对SaaS平台数据模型、云中放置策略、租户索引等进行了深入研究,主要贡献概括如下：
1.提出一种SaaS平台多租户虚拟化方式及高效映射转换的数据分层模型,通过租户无关的应用数据模型为开发商屏蔽多租户云数据管理技术细节,通过租户逻辑模型支持租户按需定制数据模式以及各业务系统数据之间的共享关系,通过逻辑存储模型为平台运营商屏蔽了云中数据节点伸缩技术,解决了SaaS应用生命周期云数据管理技术瓶颈问题。
针对SaaS平台“共享数据库、单实例多租赁、多数据节点”的需求,以及SaaS应用开发存在技术瓶颈的问题,本文建立了SaaS平台多租户虚拟化模型及数据分层模型,支持开发商面向标准SQL(Structured Query Language)编程,由平台支撑SaaS应用的按需定制及运行,能有效支持租户自主定制,数据统一管理,方便应用之间的数据共享,数据权限模型管理以及事务管理,也方便保证平台数据节点在云中的伸缩。通过原型系统验证,该多租户虚拟化模型及分层数据模型具有较高的独立性,支持开发商使用标准SQL编程；通过模式映射感知多种多租户共享存储方式,并根据元数据在各多租户存储模型之间进行切换；支持数据节点的弹性伸缩,为SaaS交付平台提供了友好数据访问模式、高一致性、高可伸缩性、高可用性。
2.提出一种多稀疏表与键值对相结合的多租户数据逻辑存储模型,以及支持租户多级定制的元数据存储模型,从根本上解决了稀疏表定制能力受限以及数据操作粒度较大的问题,降低了元数据的冗余存储,简化了租户定制过程,增强了租户按需变更能力,同时,该逻辑模型便于进行数据的分割与放置,为租户数据在云中的存储奠定了模式基础。
本文针对多租户共享存储模式下数据稀疏,进而导致存取性能下降的问题,以及租户定制能力受限、定制数据冗余存储的问题,通过划分多个稀疏表,提高了稀疏表的密集程度,避免了SaaS平台稀疏表中众多空值导致的存储空间浪费、存取性能下降及关系连接效率不高的情况,通过键值对的扩展存储机制提高了租户存储模型的定制能力,通过元数据的多级存储模型,解决了元数据冗余存储的问题,同时提高了定制效率。通过SaaS平台描述的租户信息,方便建立元数据驱动的数据分布策略。实验结果表明,本方案在用户视图列数呈正态分布的情况下,数据密集程度平均提高20%,关系连接效率随着元组数量增大而显著提高；定制数据冗余存储减少达56.7%,是一种行之有效的存储模型。
3.提出一种面向SaaS应用交付平台的云中多租户数据分割模型及动态同步迁移策略,解决了共享存储模式下无法识别SaaS应用租户,难以使用快照、日志等数据库技术进行租户数据迁移的问题,通过SaaS平台数据层面的同步迁移策略,完成租户数据的动态移植,保证云中各数据节点的负载均衡及良好的用户体验。
在云中为SaaS平台创建一个虚拟数据库,租户数据共享存储,使得云数据库无法以SaaS应用租户为单位进行数据管理,如分割、迁移、备份等。为实现数据节点的弹性伸缩,本文基于租户个体数据量较小,总体数据海量的特征,通过SaaS平台元数据驱动的租户数据分割机制,保证租户事务性操作能在单个数据节点完成,尽最大可能避免了分布式事务的处理；从数据层面构建平台的数据迁移策略,通过独立的迁移进程移植租户数据,通过数据引擎对源节点和目标节点当前事务进行同步操作,降低了宕机时间和迁移负载,保证了云中数据节点的伸缩性以及平台的整体性能。实验结果表明,本方案在数据节点数据量达到阂值50%的情况下发起数据迁移,迁移过程中租户的访问请求未受显著影响,迁移后租户的访问代价降低,平台整体性能得到了提高。
4.提出一种SaaS平台多级别、细粒度索引模型,通过SaaS交付平台租户的位置编码,迅速定位到租户所属数据节点,解决了云数据管理无法识别SaaS应用租户的问题；在租户数据节点上,建立了基于键值对模式的租户逻辑索引,解决共享表存储模式下租户索引失效、定制能力不足等问题,提高了平台数据服务的响应速度。
针对租户数据尚无有效的多级别、细粒度的索引支持的问题,本文通过SaaS平台租户节点索引、租户逻辑索引、关系数据库物理索引三部分构成了租户多级索引模型。通过租户节点索引解决了无法随机访问相关数据节点的问题,通过租户逻辑索引满足了租户索引定制、隔离等需求,通过关系数据库物理索引为逻辑索引提供高效访问机制,保证了较高的查询性能。针对租户逻辑索引,本文提出一种基于键值对存储方式的租户逻辑索引机制,基于键值对存储方式,提出元数据驱动的映射表索引模型,该模型根据租户定制需求,为租户业务数据形成各自的索引元数据,通过元数据驱动实现了索引数据的隔离及定制效果；给出索引的维护策略,根据租户数据访问请求进行索引切片,以逐渐细化的索引切片作为数据访问的基本单位,快速返回租户结果集。实验结果表明,本方案在数据访问分布均衡的情况下,索引维护及数据访问具有较好的总体性能。
With the development of cloud computing and the mature of the applications, SaaS (Software as a Service), as an important form of cloud computing, gets more and more attention. It has gradually become an important way to apply advanced technology for Small and Medium Business (SMB). Delivery platform for SaaS application promotes the vigorous development of the SaaS mode, more and more individuals or institutions rent various kinds of applications through SaaS platform. These applications cover all areas of people's lives, through applications and collaborative work between them to complete some things, including retrieval, transaction management and analysis, etc.
Currently, most matured SaaS service provider use single-instance multi-tenancy software delivery model, where thousands of tenants share one application and business data is stored in a shared database, thus the end users feel the instance is exclusive to him and not aware of the existence of other customers at the same time. The customizable storage architecture realizes the resource share at hardware-level for multi-tenant's. However, the number of tenants which a single data node can support is restricted by hardware, so it is hard to achieve the dynamic expansion of the scale when the tenant require more storage space or higher quality of service, only by upgrading the hardware. Along with the number of tenants in SaaS delivery platform as well as the amount of information grow exponentially curve, the data management in SaaS delivery platform is on the inevitable stage of changing from single data node to multi-data node in the cloud. Correspondingly, people expect more about the demand of data management in SaaS application delivery platform.
This paper focuses on the key technology of the multi-tenant cloud data management in the SaaS application delivery platform, aiming at supporting the rapid development and delivery maximumly meanwhile guaranteeing the high-speed movement of the multi-tenant SaaS application. Multi-tenant data management in the SaaS application delivery platform has its own characteristics, which can not to be solved effectively in the current cloud data management:(1) SaaS application delivery platform involves a large number of SaaS applications, the data models vary widely in different applications of business areas; Each SaaS application has many tenants who have the semblable model while having different business areas. Even though the same tenants in different development stage, the data model also has its own characteristics. Therefore, the platform must support data model to be extended effectively, at the same time, solve the problem of performance degradation due to the increasing complexity of the data management. (2)Currently, the data management under the background of the cloud is directly to tenants, who rent the data services, but the SaaS application delivery platform, where tenant rent its application. Data models for the same SaaS application are semblable between tenants, while different applications have various relations. Therefore, it is necessary to introduce the characteristics of multi-tenant data model in the cloud data management that guarantee effectively split and migration of the cloud data. (3)Current Cloud data management can't provide multi-level, fine-grained indexing mechanism for the tenants in the SaaS application, because it establish a virtual database for every user and not aware of the application tenant in the SaaS application delivery platform; At the same time, the traditional indexing mechanism has become invalid in the local data node and then can't provide an effective logical index for the tenants, making the operations on the random data of the tenants become difficulty
This dissertation aims at the multi-tenant cloud data management in the SaaS application delivery platform and research on the key issues. The main work and contributions are summarized as follows:
1. Proposing a multi-tenant virtualization approach for SaaS platform and a hierarchical data model to solve large-scale SaaS application delivery bottlenecks of cloud-based data management technology, which shielding the developers' perception to the cloud data management technique and supporting the tenant to customize its system on demand.
For the demand of "shared database, single-instance multi-tenancy" in the platform and technical bottlenecks of SaaS application development, the paper establishes application layer multi-tenant virtualization approach and hierarchical data model,which can support development through standard SQL and effectively support the tenants'customization, the unified management of data, and facilitate data sharing between applications, data management rights model and transaction management, but also easy to insure the data node stretching in the cloud. The prototype system shows that the architecture has high independence, which supports development through standard SQL; Enables the perception of various multi-tenant sharing storage methods through mapping technique according to the metadata, supporting the elastic of the data node, and providing the friendly programming model, high consistency, high scalability and high availability for SaaS application delivery platform.
2. A logical model of multi-tenant cloud data on the combination of the multi-sparse and key-value, as well as a metadata storage model on this basis, which effective solving the problem of redundant storage caused by multi-tenant shared sparse tables and metadata, at the same time getting higher performance.
For the problems in multi-tenant storage model, including sparse data, customization limitation, metadata redundancy and etc, the paper divides data to many sparse tables to increase the intensity of sparse table, which solving the waste of storage space because of null values in sparse tables, as well as the decrease of the access performance and inefficiency of relation join operations in the SaaS platform; By the expansion storage mechanism of key-value, solve the limitations in the customization of the sparse table; By multi-level metadata storage mechanism, solve the problem of redundant storage of metadata. Experimental results show that if the number of columns in the tenant view is normally distributed, the data intensive degree could be average improved 20%, relation join efficiency will be improved along with the increase of tuples, and redundant storage of the customized data will decrease down to 56.7%. In all, it is an effective storage model.
3. A multi-tenant partition model and dynamic data migration strategy in the SaaS application delivery platform, reduces the numbers of distributed transaction, simultaneously ensure the scalability of the data nodes in cloud and the overall performance of the platform.
If found a virtual database for SaaS in the cloud, the data of tenant is shared stored, so the cloud database can't manage the data in every SaaS application tenant, such as partition、shifting and backup. Metadata-driven data partitioning mechanism for tenants in the SaaS platform ensure that the transactional operation for tenant can complete within a single node, and largely avoid the distributed transaction processing; Dynamic data migration strategy moving the data partition belong to the tenant in the cloud to ensure the load balance of all the data nodes and efficient operation of the platform. This paper provides a better user experience. Experimental results show that the data migration happens when data volume in the data nodes reaches the threshold value 50%, requests from tenants were not significantly affected during the migration. Access costs for tenants reduce after migration, and the overall performance of the platform has been improved.
4. A multi-level, fine-grained indexing mechanism based on SaaS platform. It improve the data service performance of the platform through quickly locate to the data node belong to tenant and effectively solve some issues in local data node, such as tenant index failure and limited customization under share tables storage mode.
The multi-level, fine-grained indexing mechanism based on SaaS platform including three parts:tenant node index, tenant logical index, relational database physical index. The tenant node index solves the problem that cannot random access relevant data nodes of tenant. The tenant logical index satisfies the index customization, isolation and other demands. The relational database physical index provides efficient access mechanism for logical index and guarantee higher query performance of metadata. For tenant logical index, this paper proposes the metadata-driven mapping table index mechanism based on the key-value storage mode. This model forms the index metadata for the tenant's business data, according to requirements of tenant and realizes the isolation and customization of the index data through metadata driving. This paper gives the index maintenance strategy, slicing the index according to the data requests by tenants, using the gradually thinning index slice as the base unit of data access and to return the result set quickly. Experimental results show that the index maintenance and data access with better overall performance under the condition that distribution of the data access is balanced.
目前,成熟的SaaS服务提供商多采用一对多的软件交付模式,成千上万租户共享一个应用,业务数据存储在服务提供商的共享数据库中,最终用户感受不到所使用的实例在同一时间也为其他客户所共享。支持租户定制的多租户共享存储架构,实现了从操作系统到数据结构等各个级别的资源共享,充分利用了硬件、数据库等资源,然而单个节点所能支持的租户数量受到硬件限制,当租户需要更多存储空间和更高服务质量时,需要采用升级硬件的方式实现,难以实现规模的动态扩展。随着SaaS交付平台租户数目及信息数据量呈几何曲线性增长,SaaS交付平台数据管理正处于由单数据节点往云中多数据节点转变的必然阶段,而相应地对SaaS应用交付平台数据管理能力的期望值也越来越高。
本文致力于SaaS应用交付平台中多租户云数据管理关键技术的研究,目标在于最大限度地支持快速开发与交付,保障SaaS应用多租户的高效运行。SaaS应用交付平台多租户云数据管理具有自身的特点,现有云数据管理方法不能有效解决以下问题：(1)SaaS平台租户数据弹性扩展问题。SaaS应用交付平台涉及到大量的SaaS应用,各应用业务领域不同,数据模式必然千差万别；每个SaaS应用拥有众多的租户,租户在同一应用下的模式类似,但业务又不尽相同；即便是同一租户在不同的发展阶段,数据模式也会有各自的特点。因此,平台要支持数据存储模型的弹性扩展,同时,还要解决因为数据管理复杂度的增加而导致的性能下降问题,保证应用的高效运转。(2)现有云数据放置策略未引入SaaS特征。面对SaaS交付平台的海量数据,云数据库分配云中的多个数据节点为其提供服务,所有SaaS应用所有租户的数据作为一个整体被分割放置到各个节点上,由于SaaS平台租户共享存储的特点,云数据库无法根据数据模式对其进行虚拟化,租户做为独立个体的特征往往被忽略,从而导致多租户数据的混合放置以及单个租户数据的分散,增加了租户数据访问及应用之间数据共享的代价,因此,需要在云数据管理中引入平台租户数据的特征,才能有效进行SaaS平台数据分割及云数据管理的弹性伸缩。(3)缺少多级别、细粒度的SaaS平台租户索引支持。现有的云数据管理机制针对每个用户建立一个虚拟数据库,意识不到SaaS应用交付平台中的应用租户,无法精确的定位到租户数据节点上；同时,在租户数据共享存储的局部节点上,传统的索引机制已经失效,无法提供有效的租户逻辑索引,使得租户的随机数据操作变的困难。
本文以SaaS应用交付平台中多租户云数据管理为目标,对SaaS平台数据模型、云中放置策略、租户索引等进行了深入研究,主要贡献概括如下：
1.提出一种SaaS平台多租户虚拟化方式及高效映射转换的数据分层模型,通过租户无关的应用数据模型为开发商屏蔽多租户云数据管理技术细节,通过租户逻辑模型支持租户按需定制数据模式以及各业务系统数据之间的共享关系,通过逻辑存储模型为平台运营商屏蔽了云中数据节点伸缩技术,解决了SaaS应用生命周期云数据管理技术瓶颈问题。
针对SaaS平台“共享数据库、单实例多租赁、多数据节点”的需求,以及SaaS应用开发存在技术瓶颈的问题,本文建立了SaaS平台多租户虚拟化模型及数据分层模型,支持开发商面向标准SQL(Structured Query Language)编程,由平台支撑SaaS应用的按需定制及运行,能有效支持租户自主定制,数据统一管理,方便应用之间的数据共享,数据权限模型管理以及事务管理,也方便保证平台数据节点在云中的伸缩。通过原型系统验证,该多租户虚拟化模型及分层数据模型具有较高的独立性,支持开发商使用标准SQL编程；通过模式映射感知多种多租户共享存储方式,并根据元数据在各多租户存储模型之间进行切换；支持数据节点的弹性伸缩,为SaaS交付平台提供了友好数据访问模式、高一致性、高可伸缩性、高可用性。
2.提出一种多稀疏表与键值对相结合的多租户数据逻辑存储模型,以及支持租户多级定制的元数据存储模型,从根本上解决了稀疏表定制能力受限以及数据操作粒度较大的问题,降低了元数据的冗余存储,简化了租户定制过程,增强了租户按需变更能力,同时,该逻辑模型便于进行数据的分割与放置,为租户数据在云中的存储奠定了模式基础。
本文针对多租户共享存储模式下数据稀疏,进而导致存取性能下降的问题,以及租户定制能力受限、定制数据冗余存储的问题,通过划分多个稀疏表,提高了稀疏表的密集程度,避免了SaaS平台稀疏表中众多空值导致的存储空间浪费、存取性能下降及关系连接效率不高的情况,通过键值对的扩展存储机制提高了租户存储模型的定制能力,通过元数据的多级存储模型,解决了元数据冗余存储的问题,同时提高了定制效率。通过SaaS平台描述的租户信息,方便建立元数据驱动的数据分布策略。实验结果表明,本方案在用户视图列数呈正态分布的情况下,数据密集程度平均提高20%,关系连接效率随着元组数量增大而显著提高；定制数据冗余存储减少达56.7%,是一种行之有效的存储模型。
3.提出一种面向SaaS应用交付平台的云中多租户数据分割模型及动态同步迁移策略,解决了共享存储模式下无法识别SaaS应用租户,难以使用快照、日志等数据库技术进行租户数据迁移的问题,通过SaaS平台数据层面的同步迁移策略,完成租户数据的动态移植,保证云中各数据节点的负载均衡及良好的用户体验。
在云中为SaaS平台创建一个虚拟数据库,租户数据共享存储,使得云数据库无法以SaaS应用租户为单位进行数据管理,如分割、迁移、备份等。为实现数据节点的弹性伸缩,本文基于租户个体数据量较小,总体数据海量的特征,通过SaaS平台元数据驱动的租户数据分割机制,保证租户事务性操作能在单个数据节点完成,尽最大可能避免了分布式事务的处理；从数据层面构建平台的数据迁移策略,通过独立的迁移进程移植租户数据,通过数据引擎对源节点和目标节点当前事务进行同步操作,降低了宕机时间和迁移负载,保证了云中数据节点的伸缩性以及平台的整体性能。实验结果表明,本方案在数据节点数据量达到阂值50%的情况下发起数据迁移,迁移过程中租户的访问请求未受显著影响,迁移后租户的访问代价降低,平台整体性能得到了提高。
4.提出一种SaaS平台多级别、细粒度索引模型,通过SaaS交付平台租户的位置编码,迅速定位到租户所属数据节点,解决了云数据管理无法识别SaaS应用租户的问题；在租户数据节点上,建立了基于键值对模式的租户逻辑索引,解决共享表存储模式下租户索引失效、定制能力不足等问题,提高了平台数据服务的响应速度。
针对租户数据尚无有效的多级别、细粒度的索引支持的问题,本文通过SaaS平台租户节点索引、租户逻辑索引、关系数据库物理索引三部分构成了租户多级索引模型。通过租户节点索引解决了无法随机访问相关数据节点的问题,通过租户逻辑索引满足了租户索引定制、隔离等需求,通过关系数据库物理索引为逻辑索引提供高效访问机制,保证了较高的查询性能。针对租户逻辑索引,本文提出一种基于键值对存储方式的租户逻辑索引机制,基于键值对存储方式,提出元数据驱动的映射表索引模型,该模型根据租户定制需求,为租户业务数据形成各自的索引元数据,通过元数据驱动实现了索引数据的隔离及定制效果；给出索引的维护策略,根据租户数据访问请求进行索引切片,以逐渐细化的索引切片作为数据访问的基本单位,快速返回租户结果集。实验结果表明,本方案在数据访问分布均衡的情况下,索引维护及数据访问具有较好的总体性能。
With the development of cloud computing and the mature of the applications, SaaS (Software as a Service), as an important form of cloud computing, gets more and more attention. It has gradually become an important way to apply advanced technology for Small and Medium Business (SMB). Delivery platform for SaaS application promotes the vigorous development of the SaaS mode, more and more individuals or institutions rent various kinds of applications through SaaS platform. These applications cover all areas of people's lives, through applications and collaborative work between them to complete some things, including retrieval, transaction management and analysis, etc.
Currently, most matured SaaS service provider use single-instance multi-tenancy software delivery model, where thousands of tenants share one application and business data is stored in a shared database, thus the end users feel the instance is exclusive to him and not aware of the existence of other customers at the same time. The customizable storage architecture realizes the resource share at hardware-level for multi-tenant's. However, the number of tenants which a single data node can support is restricted by hardware, so it is hard to achieve the dynamic expansion of the scale when the tenant require more storage space or higher quality of service, only by upgrading the hardware. Along with the number of tenants in SaaS delivery platform as well as the amount of information grow exponentially curve, the data management in SaaS delivery platform is on the inevitable stage of changing from single data node to multi-data node in the cloud. Correspondingly, people expect more about the demand of data management in SaaS application delivery platform.
This paper focuses on the key technology of the multi-tenant cloud data management in the SaaS application delivery platform, aiming at supporting the rapid development and delivery maximumly meanwhile guaranteeing the high-speed movement of the multi-tenant SaaS application. Multi-tenant data management in the SaaS application delivery platform has its own characteristics, which can not to be solved effectively in the current cloud data management:(1) SaaS application delivery platform involves a large number of SaaS applications, the data models vary widely in different applications of business areas; Each SaaS application has many tenants who have the semblable model while having different business areas. Even though the same tenants in different development stage, the data model also has its own characteristics. Therefore, the platform must support data model to be extended effectively, at the same time, solve the problem of performance degradation due to the increasing complexity of the data management. (2)Currently, the data management under the background of the cloud is directly to tenants, who rent the data services, but the SaaS application delivery platform, where tenant rent its application. Data models for the same SaaS application are semblable between tenants, while different applications have various relations. Therefore, it is necessary to introduce the characteristics of multi-tenant data model in the cloud data management that guarantee effectively split and migration of the cloud data. (3)Current Cloud data management can't provide multi-level, fine-grained indexing mechanism for the tenants in the SaaS application, because it establish a virtual database for every user and not aware of the application tenant in the SaaS application delivery platform; At the same time, the traditional indexing mechanism has become invalid in the local data node and then can't provide an effective logical index for the tenants, making the operations on the random data of the tenants become difficulty
This dissertation aims at the multi-tenant cloud data management in the SaaS application delivery platform and research on the key issues. The main work and contributions are summarized as follows:
1. Proposing a multi-tenant virtualization approach for SaaS platform and a hierarchical data model to solve large-scale SaaS application delivery bottlenecks of cloud-based data management technology, which shielding the developers' perception to the cloud data management technique and supporting the tenant to customize its system on demand.
For the demand of "shared database, single-instance multi-tenancy" in the platform and technical bottlenecks of SaaS application development, the paper establishes application layer multi-tenant virtualization approach and hierarchical data model,which can support development through standard SQL and effectively support the tenants'customization, the unified management of data, and facilitate data sharing between applications, data management rights model and transaction management, but also easy to insure the data node stretching in the cloud. The prototype system shows that the architecture has high independence, which supports development through standard SQL; Enables the perception of various multi-tenant sharing storage methods through mapping technique according to the metadata, supporting the elastic of the data node, and providing the friendly programming model, high consistency, high scalability and high availability for SaaS application delivery platform.
2. A logical model of multi-tenant cloud data on the combination of the multi-sparse and key-value, as well as a metadata storage model on this basis, which effective solving the problem of redundant storage caused by multi-tenant shared sparse tables and metadata, at the same time getting higher performance.
For the problems in multi-tenant storage model, including sparse data, customization limitation, metadata redundancy and etc, the paper divides data to many sparse tables to increase the intensity of sparse table, which solving the waste of storage space because of null values in sparse tables, as well as the decrease of the access performance and inefficiency of relation join operations in the SaaS platform; By the expansion storage mechanism of key-value, solve the limitations in the customization of the sparse table; By multi-level metadata storage mechanism, solve the problem of redundant storage of metadata. Experimental results show that if the number of columns in the tenant view is normally distributed, the data intensive degree could be average improved 20%, relation join efficiency will be improved along with the increase of tuples, and redundant storage of the customized data will decrease down to 56.7%. In all, it is an effective storage model.
3. A multi-tenant partition model and dynamic data migration strategy in the SaaS application delivery platform, reduces the numbers of distributed transaction, simultaneously ensure the scalability of the data nodes in cloud and the overall performance of the platform.
If found a virtual database for SaaS in the cloud, the data of tenant is shared stored, so the cloud database can't manage the data in every SaaS application tenant, such as partition、shifting and backup. Metadata-driven data partitioning mechanism for tenants in the SaaS platform ensure that the transactional operation for tenant can complete within a single node, and largely avoid the distributed transaction processing; Dynamic data migration strategy moving the data partition belong to the tenant in the cloud to ensure the load balance of all the data nodes and efficient operation of the platform. This paper provides a better user experience. Experimental results show that the data migration happens when data volume in the data nodes reaches the threshold value 50%, requests from tenants were not significantly affected during the migration. Access costs for tenants reduce after migration, and the overall performance of the platform has been improved.
4. A multi-level, fine-grained indexing mechanism based on SaaS platform. It improve the data service performance of the platform through quickly locate to the data node belong to tenant and effectively solve some issues in local data node, such as tenant index failure and limited customization under share tables storage mode.
The multi-level, fine-grained indexing mechanism based on SaaS platform including three parts:tenant node index, tenant logical index, relational database physical index. The tenant node index solves the problem that cannot random access relevant data nodes of tenant. The tenant logical index satisfies the index customization, isolation and other demands. The relational database physical index provides efficient access mechanism for logical index and guarantee higher query performance of metadata. For tenant logical index, this paper proposes the metadata-driven mapping table index mechanism based on the key-value storage mode. This model forms the index metadata for the tenant's business data, according to requirements of tenant and realizes the isolation and customization of the index data through metadata driving. This paper gives the index maintenance strategy, slicing the index according to the data requests by tenants, using the gradually thinning index slice as the base unit of data access and to return the result set quickly. Experimental results show that the index maintenance and data access with better overall performance under the condition that distribution of the data access is balanced.
引文
[1]Craig D Weissman, Craig D Weissman. "The Design of the Force.com Multitenant Internet Application Development Platform//SIGMOD, Providence, Rhode Island, USA,2009, ACM 2009:889-896
[2]Frederick Chong, Gianpaolo Carraro. Architecture strategies for catching the long tail:http://msdn2.microsoft.com/zh-cn/architecture/aa479069.aspx,2006.4
[3]saas grid.http://saasgrid.com/
[4]Oracle Platform for SaaS, http://www.oracle.com/us/technologies/saas/index.html
[5]Corent, http://www.multi-tenancy.com/
[6]Changjie Guo, Wei Sun. "A Framework for Native Multi-Tenancy Application Development and Management", CEC/EEE 2007:551-558
[7]Steve Fisher. The Architecture of the Apex Platform, salesforce.com's Platform for Building On-Demand Applications. In:ICSE2007, p 3,2007, Companion Volume
[8]Frederick Chong, Gianpaolo Carraro,"Multi-Tenant Data Architecture", http://msdn. microsoft. com/en-us/library/aa479086.aspx,2006.7
[9]Stefan Aulbach, Dean Jacob, s Alfons Kemper, Michael Seibold, "A Comparison of Flexible Schemas for Software as a Service",SIGMOD'09, June 29-July 2,2009, Providence, Rhode Island, USA
[10]昌中作,徐悦,戴钢。基于SaaS模式公共服务平台多用户数据结构的研究//计算机系统应用,2008.2：7-11
[11]Hakan Hacigumus, Sharad Mehrotra,"Providing Database as a Service", ICDE 2002:29-38
[12]Mei Hui, Dawei Jiang, Guoliang Li, Yuan Zhou, "Supporting Database Applications as a Service", ICDE 2009:832-843
[13]SQL Azure, http://www.microsoft.com/en-us/sqlazure/default.aspx
[14]Sudipto Das, Divyakant Agrawal, and Amr El Abbadi, "ElasTraS:An Elastic Transactional Data Store in the Cloud", In USENIX HotCloud,2009.2
[15]Divyakant Agrawal, Amr El Abbadi, Shyam Antony, and Sudipto Das, "Data Management Challenges in Cloud Computing Infrastructures", In DNIS, pages 1-10,2010.3
[16]NoSQL Databases, http://nosql-database.org/
[17]NoSQL from wikipedia, http://en.wikipedia.org/wiki/NoSQL
[18]Hadoop/Hbase, http://hadoop.apache.org/
[19]Cassandra, http://cassandra.apache.org/
[20]Hypertable, http://hypertable.org/
[21]Cloudera, http://www.cloudera.com/
[22]Amazone SimpleDB, http://aws.amazon.com/simpledb/
[23]CouchDB, http://couchdb.apache.org/
[24]MongoDB, http://www.mongodb.org/
[25]Terrastore, http://code.google.com/p/terrastore/
[26]ThruDB, http://code.google.com/p/thrudb/
[27]OrientDB, http://www.orientechnologies.com/
[28]Azure Table Storage, http://msdn.microsoft.com/en-us/library/ddl79423.aspx
[29]MEMBASE, http://www.membase.org/
[30]MemcacheDB, http://memcachedb.org/
[31]Neo4j, http://neo4j.org/
[32]Michael Stonebraker, "SQL databases v. NoSQL databases", Commun, ACM (CACM) 53(4):10-11(2010)
[33]Neal Leavitt, "Will NoSQL Databases Live Up to Their Promise? ", IEEE Computer (COMPUTER) 43(2):12-14 (2010)
[34]Database. com,http://www. database.com/
[35]Kwok Thomas, Nguyen Thao, Lam Linh, "A software as a service with multi-tenancy support for an electronic contract management application", SCC2008, v2, p 179-186,2008.
[36]Sanjay Ghemawat, Howard Gobioff, Shun-Talc Leung, "The googIe file system",http://labs.google.com/papers/gfs—sosp2003.pdf.
[37]Mike Burrows, "The chubby lock service for loosely-coupled distributed systems", http://labs.google.com/papers/chubby-osdi06.pdf
[38]Jeffrey Dean, Sanjay Ghemawat, "Distributed programming with mapreduce". Oram A, Wilson G, eds. Beautiful Cede. Sebastopol:O'Reilly Media,Inc。2007:371-384
[39]Fan Yang, Jayavel Shanmugasundaram, Ramana Yemeni, "A Scalable Data Platform for a Large Number of Small Applications", In CIDR,2009.1,3,11.
[40]L G Alex Sung, Nabeel Ahmed, Rolando Blanco, "A Survey of Data Management in Peer-to-Peer Systems [R] ". Canada:University of Waterloo, 2005.
[41]裴莹,徐俊刚.基于服务的企业标准化培训平台.计算机应用与软件2010(1)
[42]万江川,郭建胜,任婷婷.基于SaaS模式的航空装备维修保障综合平台相关技术研究. 航空维修与工程2009(6)
[43]许元虎,余本功,沈浩杰.WSS平台对SaaS模式软件开发的支持.计算机与现代化2009(5)
[44]丁文定.基于服务集成管理的SaaS解决方案.计算机与现代化2010(1)
[45]Stefan Aulbach, Torsten Grust, Dean Jacobs, Alfons Kemper, Jan Rittinger, "Multi-tenant databases for software as a service:schema-mapping techniques" SIGMOD 2008:1195-1206
[46]Crab, http://saascrab.codeplex.com/
[47]Rakesh Agrawal, Amit Somani, Yirong Xu, "Storage and Querying of E-Commerce Data", In VLDB, pages 149-158,2001
[48]Eric Chu, Jennifer Beckmann, and Jeffrey Naughton, "The Case for a Wide-Table Approach to Manage Sparse Relational Data Sets", In SIGMOD, pages 821-832,2007.
[49]Jennifer L. Beckmann, Alan Halverson, Rajasekar Krishnamurthy, Jeffrey F. Naughton, "Extending RDBMSs To Support Sparse Datasets Using an Interpreted Attribute Storage Format", In ICDE, page 58,2006.
[50]Giuseppe DeCandia, "Dynamo:Amazon's highly available key-value store", In SOSP,2007.
[51]Larisa Shwartz, Naga Ayachitula, Melissa J. Buco, Genady Grabarnik, Maheswaran Surendra, Christopher Ward, Steve Weinberger, "IT Service Provider's Multi-Customer and Multi-Tenant Environments". CEC/EEE 2007:559-566
[52]Ralph Mietzner, Frank Leymann, Mike P Papazoglou, "Defining Composite Configurable SaaS Application Packages Using SCA, Variability Descriptors and Multi-tenancy Patterns". ICIW 2008:156-161
[53]Jurgen Muller, Jens Kruger, Sebastian Enderlein, Marco Helmich, Alexander Zeier, "Customizing Enterprise Software as a Service Applications:Back-End Extension in a Multi-tenancy Environment". ICEIS 2009:66-77
[54]Serdar Cabuk, Chris I Dalton, Konrad Eriksson, Dirk Kuhlmann, HariGovind V. Ramasamy, Gianluca Ramunno, Ahmad-Reza Sadeghi, Matthias Schunter, Christian Stuble, "Towards automated security policy enforcement in multi-tenant virtual data centers". Journal of Computer Security (JCS) 18(1):89-121 (2010)
[55]Juan Du, Xiaohui Gu, Douglas S Reeves, "Highly available component sharing in large-scale multi-tenant cloud systems", HPDC 2010:85-94
[56]Zhi Hu Wang, Chang Jie Guo, "A study and performance evaluation of the multi-tenant data tier design patterns for service oriented computing", IEEE International Conference on e_Business Engineering,2008. Piscataway, United States, IEEE CS 2008:94-101
[57]Erin TenWolde, "Worldwide Software on Demand 2007-2011 Forecast:A Preliminary Look at Delivery Model Performance", IDC No.206240,2007, IDC Report.
[58]Martin Grund, Matthieu Schapranow, Jens Krueger, Jan Schaffner, Anja Bog, "Shared Table Access Pattern Analysis for Multi-Tenant Applications". IEEE Symposium on Advanced Management of Information for Globalized Enterprises, 2008,1-5
[59]Salesforce.com, http://www.salesforce.com
[60]叶伟等著.互联网时代的软件革命——SaaS架构设计.北京：电子工业 出版社,2009年01月第1版.
[61]许向阳,张勇王,元珍.并行PDBMS的数据划分方法.计算机工程与应用2001(8)
[62]Carlo Curino, Evan Jones, Yang Zhang, Eugene Wu, Sam Madden, "Relational Cloud:The Case for a Database Service ".Technical Report 2010-14, CSAIL, MIT,2010.4,11
[63]Carlo Curino, Evan Jones, Yang Zhang, Sam Madden. "Schism:a Workload-Driven Approach to Database Replication and Partitioning". In VLDB, pages 48-57,2010
[64]Jan-Jan Wu, Pangfeng Liu, Yi-Chien Chung, "Metadata Partitioning for Large-Scale Distributed Storage Systems".2010 IEEE 3rd International Conference on Cloud Computing,212-219
[65]Aaron Elmore, Sudipto Das, Divyakant Agrawal, Amr El Abbadi, "Who's Driving this Cloud? Towards Efficient Migration for Elastic and Autonomic Multitenant Databases", In:Technical Report 2010-05, CS, UCSB 2010, http://www.cs.ucsb.edu/research/tech_reports/.9,11
[66]Brian F. Cooper, Raghu Ramakrishnan, Utkarsh Srivastava, Adam Silberstein, "PNUTS:Yahoo!'s hosted data serving platform".Proc. VLDB Endow. 1(2):1277-1288,2008.2,3,11
[67]Adam Silberstein, Brian F. Cooper, Utkarsh Srivastava, Erik Vee, Ramana Yemeni, Raghu Ramakrishnan, "Efficient bulk insertion into a distributed ordered table". Proc. SIGMOD,2008.
[68]Tobias Scholl, Angelika Reiser, "Workload-aware data partitioning in community-driven data grids". In:EDBT,2009.
[69]G. H. Sockut and I. B. R, "Online reorganization of databases". In:ACM Comput. Surv, pages 1-136,2009.
[70]T. Kraska, M. Hentschel, G. Alonso, and D. Kossmann, "Consistency rationing in the cloud:Pay only when it matters", PVLDB,2(1),2009.
[71]Sudipto Das, Shashank Agarwal, Divyakant Agrawal, Amr El Abbadi, "ElasTraS:An Elastic, Scalable, and Self Managing Transactional Database for the Cloud". In:Technical Report 2010-04, CS, UCSB,2010. http://www.cs.ucsb.edu/research/tech_reports/.1,2,7,8,9,11
[72]VoltDB, http://voltdb.com/
[73]Dean Jacobs, Stefan Aulbach, "Ruminations on Multi-Tenant Databases", BTW 2007:514-521
[74]H Liu, H Jin, X Liao, L Hu, C Yu. "Live migration of virtual machine based on full system trace and replay". In HPDC, pages 101-110,2009.11
[75]C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield. "Live migration of virtual machines". In NSDI, pages 273-286,2005.2, 4,11,14
[76]Khuzaima Daudjee, Kenneth Salem, "Lazy database replication with snapshot isolation", In Proc. VLDB,2006.
[77]Sudipto Das, "Live Database Migration for Elasticity in a Multitenant Database for Cloud Platforms", In:UCSB Computer Science Technical Report 2010-09.
[78]Patrick Hunt, Mahadev Konar, "ZooKeeper:Wait-free Coordination for Internet-scale Systems", In USENIX Annual Technical Conference,2010.8,9
[79]The Transaction Processing Performance Council, TPC-C benchmark (Version 5.10.1),2009.2,9,10
[80]Daniel J.Abadi, "Data Management in the Cloud:Limitations and Opportunities". IEEE Data Eng. Bull. (DEBU) 32(1):3-12 (2009)
[81]Sudipto Das, Divyakant Agrawal, Amr El Abbadi, "G-Store:A Scalable Data Store for Transactional Multi key Access in the Cloud". In ACM SoCC, pages 163-174,2010.3,11
[82]Li Xiong, Hengqing Tong, "Customer Satisfaction Index Assessment Based on Cloud Computing". ISCID 2009:330-333
[83]Sai Wu, Kun-Lung Wu, "An Indexing Framework for Efficient Retrieval on the Cloud". IEEE Data Eng. Bull. (DEBU) 32(1):75-82 (2009)
[84]Shih-Tsun Chu, Chao-Chun Yeh, Chun-Lung Huang, "A Cloud-Based Trajectory Index Scheme". ICEBE 2009:602-607
[85]Xiangyu Zhang, Jing Ai, Zhongyuan Wang, Jiaheng Lu, Xiaofeng Meng, "An efficient multi-dimensional index for cloud data management". CloudDb 2009:17-24
[86]Yonggang Wang, Sheng Wang, Daliang Zhou, "Retrieving and Indexing Spatial Data in the Cloud Computing Environment". CloudCom 2009:322-331
[87]Jinbao Wang, Sai Wu, Hong Gao, Jianzhong Li, Beng Chin Ooi, "Indexing multi-dimensional data in a cloud system". SIGMOD 2010:591-602
[88]Sai Wu, Dawei Jiang, Beng Chin Ooi, Kun-Lung Wu, "Efficient B-tree Based Indexing for Cloud Data Processing". PVLDB 3(1):1207-1218 (2010)
[89]Sudipto Das, Divyakant Agrawal, Amr El Abbadi, "G-Store:a scalable data store for transactional multi key access in the cloud". SoCC 2010:163-174
[90]Hoang Tam Vo, Chun Chen, Beng Chin Ooi, "Towards Elastic Transactional Cloud Storage with Range Query Support",PVLDB 3(1):506-517 (2010)
[91]Mike Stonebraker, Daniel Abadi, "C-store:a column oriented dbms", VLDB, Trondheim, Norway,2005. ACM 2005:553-564
[92]Daniel Abadi, "Materialization strategies in a column-oriented dbms", ICDE, Istanbul, Turkey,2007. IEEE 2007:466-475
[93]Daniel Abadi, Samuel Madden, "Integrating compression and execution in column-oriented database systems", SIGMOD, Chicago, Illinois, USA,2006, ACM 2006:671-682
[94]Martin Kersten, Stefan Manegold, "Cracking the database store", CIDR, Asilomar, CA, USA,2005. www.crdrdb.org 2005:213-224
[95]Stratos Idreos, Martin Kersten, Stefan Manegold, "Database cracking", CIDR, Asilomar, CA, USA,2007. www.crdrdb.org 2007:68-78
[96]Jianguo Sun, Tinghua Ai, Chuanyan Zhao, Haowen Yan, "Assessing vegetation degradation in Loess Plateau by using potential vegetation index" IGARSS 2007.July 2007
[97]Min Fang, Yong Liu, Chunpeng Jin, Zhang Ping, "Study on the index system of large-scale non-grid-connected wind power suitable industry", World Non-Grid-Connected Wind Power and Energy Conference,2009. Sept.2009.
[98]Weisshaar, A, "Impedance boundary method of moments for accurate and efficient analysis of planar graded-index optical waveguides", Lightwave Technology, V12, Issue 11, Nov 1994.
[99]Das K K, Arif M, Haider A F M Y, Alam M S, "An interferometric technique for measuring the refractive index profile of a graded index optical fiber", Aerospace and Electronics Conference,1994. NAECON 1994.May 1994
[100]Lueker, George S, "A data structure for orthogonal range queries", Foundations of Computer Science,1978.Oct.1978
[101]Xiangyu Zhang, Jing Ai, Zhongyuan Wang, Jiaheng Lu, Xiaofeng Meng, 'An efficient multi-dimensional index for cloud data management", In:CloudDB, Hong Kong, China,2009. ACM 2009:17-24
[102]Kiyoung Yang, Shahabi.C, "A multilevel distance-based index structure for multivariate time series", Temporal Representation and Reasoning,2005, June 2005
[103]Wattanakitrungroj Niwan, Vanichayobon Sirirut, "Dual Bitmap Index: Space-Time Efficient Bitmap Index for Equality and Membership Queries", Communications and Information Technologies,2006. ISCIT'06.Sept 2006
[104]Weahama W, Vanichayobon S, Manfuekphan J, "Using Data Clustering to Optimize Scatter Bitmap Index for Membership Queries", Computer and Automation Engineering,2009. March 2009
[105]Sainui J, Vanichayobon S, Wattanakitrungroj N, "Optimizing Encoded Bitmap Index Using Frequent Itemsets Mining", Computer and Electrical Engineering,2008. Dec 2008
[106]O'Neil.E, O'Neil.P, Kesheng Wu, "Bitmap Index Design Choices and Their Performance Implications", Database Engineering and Applications Symposium, 2007. IDEAS 2007.Sept 2007
[107]David G. Campbell, Gopal Kakivaya, Nigel Ellis, "Extreme Scale with Full SQL Language Support in Microsoft SQL Azure ", In SIGMOD'10, June 6-10, 2010, Indianapolis, Indiana, USA.
[108]Wolfgang Nejdl, Boris Wolf, Changtao Qu, Stefan Decker, Michael Sintek,"EDUTELLA:A P2P Networking Infrastructure Based on RDF", SIGMOD'10, June 6-10,2010, Indianapolis, Indiana, USA.
[109]Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, Hari Balakrishnan, "Chord:A scalable Peer-to-Peer lookup service for intemet applications", In Proc. SIGCOMM, San Diego, CA, Aug.2001.pp149-160.
[2]Frederick Chong, Gianpaolo Carraro. Architecture strategies for catching the long tail:http://msdn2.microsoft.com/zh-cn/architecture/aa479069.aspx,2006.4
[3]saas grid.http://saasgrid.com/
[4]Oracle Platform for SaaS, http://www.oracle.com/us/technologies/saas/index.html
[5]Corent, http://www.multi-tenancy.com/
[6]Changjie Guo, Wei Sun. "A Framework for Native Multi-Tenancy Application Development and Management", CEC/EEE 2007:551-558
[7]Steve Fisher. The Architecture of the Apex Platform, salesforce.com's Platform for Building On-Demand Applications. In:ICSE2007, p 3,2007, Companion Volume
[8]Frederick Chong, Gianpaolo Carraro,"Multi-Tenant Data Architecture", http://msdn. microsoft. com/en-us/library/aa479086.aspx,2006.7
[9]Stefan Aulbach, Dean Jacob, s Alfons Kemper, Michael Seibold, "A Comparison of Flexible Schemas for Software as a Service",SIGMOD'09, June 29-July 2,2009, Providence, Rhode Island, USA
[10]昌中作,徐悦,戴钢。基于SaaS模式公共服务平台多用户数据结构的研究//计算机系统应用,2008.2：7-11
[11]Hakan Hacigumus, Sharad Mehrotra,"Providing Database as a Service", ICDE 2002:29-38
[12]Mei Hui, Dawei Jiang, Guoliang Li, Yuan Zhou, "Supporting Database Applications as a Service", ICDE 2009:832-843
[13]SQL Azure, http://www.microsoft.com/en-us/sqlazure/default.aspx
[14]Sudipto Das, Divyakant Agrawal, and Amr El Abbadi, "ElasTraS:An Elastic Transactional Data Store in the Cloud", In USENIX HotCloud,2009.2
[15]Divyakant Agrawal, Amr El Abbadi, Shyam Antony, and Sudipto Das, "Data Management Challenges in Cloud Computing Infrastructures", In DNIS, pages 1-10,2010.3
[16]NoSQL Databases, http://nosql-database.org/
[17]NoSQL from wikipedia, http://en.wikipedia.org/wiki/NoSQL
[18]Hadoop/Hbase, http://hadoop.apache.org/
[19]Cassandra, http://cassandra.apache.org/
[20]Hypertable, http://hypertable.org/
[21]Cloudera, http://www.cloudera.com/
[22]Amazone SimpleDB, http://aws.amazon.com/simpledb/
[23]CouchDB, http://couchdb.apache.org/
[24]MongoDB, http://www.mongodb.org/
[25]Terrastore, http://code.google.com/p/terrastore/
[26]ThruDB, http://code.google.com/p/thrudb/
[27]OrientDB, http://www.orientechnologies.com/
[28]Azure Table Storage, http://msdn.microsoft.com/en-us/library/ddl79423.aspx
[29]MEMBASE, http://www.membase.org/
[30]MemcacheDB, http://memcachedb.org/
[31]Neo4j, http://neo4j.org/
[32]Michael Stonebraker, "SQL databases v. NoSQL databases", Commun, ACM (CACM) 53(4):10-11(2010)
[33]Neal Leavitt, "Will NoSQL Databases Live Up to Their Promise? ", IEEE Computer (COMPUTER) 43(2):12-14 (2010)
[34]Database. com,http://www. database.com/
[35]Kwok Thomas, Nguyen Thao, Lam Linh, "A software as a service with multi-tenancy support for an electronic contract management application", SCC2008, v2, p 179-186,2008.
[36]Sanjay Ghemawat, Howard Gobioff, Shun-Talc Leung, "The googIe file system",http://labs.google.com/papers/gfs—sosp2003.pdf.
[37]Mike Burrows, "The chubby lock service for loosely-coupled distributed systems", http://labs.google.com/papers/chubby-osdi06.pdf
[38]Jeffrey Dean, Sanjay Ghemawat, "Distributed programming with mapreduce". Oram A, Wilson G, eds. Beautiful Cede. Sebastopol:O'Reilly Media,Inc。2007:371-384
[39]Fan Yang, Jayavel Shanmugasundaram, Ramana Yemeni, "A Scalable Data Platform for a Large Number of Small Applications", In CIDR,2009.1,3,11.
[40]L G Alex Sung, Nabeel Ahmed, Rolando Blanco, "A Survey of Data Management in Peer-to-Peer Systems [R] ". Canada:University of Waterloo, 2005.
[41]裴莹,徐俊刚.基于服务的企业标准化培训平台.计算机应用与软件2010(1)
[42]万江川,郭建胜,任婷婷.基于SaaS模式的航空装备维修保障综合平台相关技术研究. 航空维修与工程2009(6)
[43]许元虎,余本功,沈浩杰.WSS平台对SaaS模式软件开发的支持.计算机与现代化2009(5)
[44]丁文定.基于服务集成管理的SaaS解决方案.计算机与现代化2010(1)
[45]Stefan Aulbach, Torsten Grust, Dean Jacobs, Alfons Kemper, Jan Rittinger, "Multi-tenant databases for software as a service:schema-mapping techniques" SIGMOD 2008:1195-1206
[46]Crab, http://saascrab.codeplex.com/
[47]Rakesh Agrawal, Amit Somani, Yirong Xu, "Storage and Querying of E-Commerce Data", In VLDB, pages 149-158,2001
[48]Eric Chu, Jennifer Beckmann, and Jeffrey Naughton, "The Case for a Wide-Table Approach to Manage Sparse Relational Data Sets", In SIGMOD, pages 821-832,2007.
[49]Jennifer L. Beckmann, Alan Halverson, Rajasekar Krishnamurthy, Jeffrey F. Naughton, "Extending RDBMSs To Support Sparse Datasets Using an Interpreted Attribute Storage Format", In ICDE, page 58,2006.
[50]Giuseppe DeCandia, "Dynamo:Amazon's highly available key-value store", In SOSP,2007.
[51]Larisa Shwartz, Naga Ayachitula, Melissa J. Buco, Genady Grabarnik, Maheswaran Surendra, Christopher Ward, Steve Weinberger, "IT Service Provider's Multi-Customer and Multi-Tenant Environments". CEC/EEE 2007:559-566
[52]Ralph Mietzner, Frank Leymann, Mike P Papazoglou, "Defining Composite Configurable SaaS Application Packages Using SCA, Variability Descriptors and Multi-tenancy Patterns". ICIW 2008:156-161
[53]Jurgen Muller, Jens Kruger, Sebastian Enderlein, Marco Helmich, Alexander Zeier, "Customizing Enterprise Software as a Service Applications:Back-End Extension in a Multi-tenancy Environment". ICEIS 2009:66-77
[54]Serdar Cabuk, Chris I Dalton, Konrad Eriksson, Dirk Kuhlmann, HariGovind V. Ramasamy, Gianluca Ramunno, Ahmad-Reza Sadeghi, Matthias Schunter, Christian Stuble, "Towards automated security policy enforcement in multi-tenant virtual data centers". Journal of Computer Security (JCS) 18(1):89-121 (2010)
[55]Juan Du, Xiaohui Gu, Douglas S Reeves, "Highly available component sharing in large-scale multi-tenant cloud systems", HPDC 2010:85-94
[56]Zhi Hu Wang, Chang Jie Guo, "A study and performance evaluation of the multi-tenant data tier design patterns for service oriented computing", IEEE International Conference on e_Business Engineering,2008. Piscataway, United States, IEEE CS 2008:94-101
[57]Erin TenWolde, "Worldwide Software on Demand 2007-2011 Forecast:A Preliminary Look at Delivery Model Performance", IDC No.206240,2007, IDC Report.
[58]Martin Grund, Matthieu Schapranow, Jens Krueger, Jan Schaffner, Anja Bog, "Shared Table Access Pattern Analysis for Multi-Tenant Applications". IEEE Symposium on Advanced Management of Information for Globalized Enterprises, 2008,1-5
[59]Salesforce.com, http://www.salesforce.com
[60]叶伟等著.互联网时代的软件革命——SaaS架构设计.北京：电子工业 出版社,2009年01月第1版.
[61]许向阳,张勇王,元珍.并行PDBMS的数据划分方法.计算机工程与应用2001(8)
[62]Carlo Curino, Evan Jones, Yang Zhang, Eugene Wu, Sam Madden, "Relational Cloud:The Case for a Database Service ".Technical Report 2010-14, CSAIL, MIT,2010.4,11
[63]Carlo Curino, Evan Jones, Yang Zhang, Sam Madden. "Schism:a Workload-Driven Approach to Database Replication and Partitioning". In VLDB, pages 48-57,2010
[64]Jan-Jan Wu, Pangfeng Liu, Yi-Chien Chung, "Metadata Partitioning for Large-Scale Distributed Storage Systems".2010 IEEE 3rd International Conference on Cloud Computing,212-219
[65]Aaron Elmore, Sudipto Das, Divyakant Agrawal, Amr El Abbadi, "Who's Driving this Cloud? Towards Efficient Migration for Elastic and Autonomic Multitenant Databases", In:Technical Report 2010-05, CS, UCSB 2010, http://www.cs.ucsb.edu/research/tech_reports/.9,11
[66]Brian F. Cooper, Raghu Ramakrishnan, Utkarsh Srivastava, Adam Silberstein, "PNUTS:Yahoo!'s hosted data serving platform".Proc. VLDB Endow. 1(2):1277-1288,2008.2,3,11
[67]Adam Silberstein, Brian F. Cooper, Utkarsh Srivastava, Erik Vee, Ramana Yemeni, Raghu Ramakrishnan, "Efficient bulk insertion into a distributed ordered table". Proc. SIGMOD,2008.
[68]Tobias Scholl, Angelika Reiser, "Workload-aware data partitioning in community-driven data grids". In:EDBT,2009.
[69]G. H. Sockut and I. B. R, "Online reorganization of databases". In:ACM Comput. Surv, pages 1-136,2009.
[70]T. Kraska, M. Hentschel, G. Alonso, and D. Kossmann, "Consistency rationing in the cloud:Pay only when it matters", PVLDB,2(1),2009.
[71]Sudipto Das, Shashank Agarwal, Divyakant Agrawal, Amr El Abbadi, "ElasTraS:An Elastic, Scalable, and Self Managing Transactional Database for the Cloud". In:Technical Report 2010-04, CS, UCSB,2010. http://www.cs.ucsb.edu/research/tech_reports/.1,2,7,8,9,11
[72]VoltDB, http://voltdb.com/
[73]Dean Jacobs, Stefan Aulbach, "Ruminations on Multi-Tenant Databases", BTW 2007:514-521
[74]H Liu, H Jin, X Liao, L Hu, C Yu. "Live migration of virtual machine based on full system trace and replay". In HPDC, pages 101-110,2009.11
[75]C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield. "Live migration of virtual machines". In NSDI, pages 273-286,2005.2, 4,11,14
[76]Khuzaima Daudjee, Kenneth Salem, "Lazy database replication with snapshot isolation", In Proc. VLDB,2006.
[77]Sudipto Das, "Live Database Migration for Elasticity in a Multitenant Database for Cloud Platforms", In:UCSB Computer Science Technical Report 2010-09.
[78]Patrick Hunt, Mahadev Konar, "ZooKeeper:Wait-free Coordination for Internet-scale Systems", In USENIX Annual Technical Conference,2010.8,9
[79]The Transaction Processing Performance Council, TPC-C benchmark (Version 5.10.1),2009.2,9,10
[80]Daniel J.Abadi, "Data Management in the Cloud:Limitations and Opportunities". IEEE Data Eng. Bull. (DEBU) 32(1):3-12 (2009)
[81]Sudipto Das, Divyakant Agrawal, Amr El Abbadi, "G-Store:A Scalable Data Store for Transactional Multi key Access in the Cloud". In ACM SoCC, pages 163-174,2010.3,11
[82]Li Xiong, Hengqing Tong, "Customer Satisfaction Index Assessment Based on Cloud Computing". ISCID 2009:330-333
[83]Sai Wu, Kun-Lung Wu, "An Indexing Framework for Efficient Retrieval on the Cloud". IEEE Data Eng. Bull. (DEBU) 32(1):75-82 (2009)
[84]Shih-Tsun Chu, Chao-Chun Yeh, Chun-Lung Huang, "A Cloud-Based Trajectory Index Scheme". ICEBE 2009:602-607
[85]Xiangyu Zhang, Jing Ai, Zhongyuan Wang, Jiaheng Lu, Xiaofeng Meng, "An efficient multi-dimensional index for cloud data management". CloudDb 2009:17-24
[86]Yonggang Wang, Sheng Wang, Daliang Zhou, "Retrieving and Indexing Spatial Data in the Cloud Computing Environment". CloudCom 2009:322-331
[87]Jinbao Wang, Sai Wu, Hong Gao, Jianzhong Li, Beng Chin Ooi, "Indexing multi-dimensional data in a cloud system". SIGMOD 2010:591-602
[88]Sai Wu, Dawei Jiang, Beng Chin Ooi, Kun-Lung Wu, "Efficient B-tree Based Indexing for Cloud Data Processing". PVLDB 3(1):1207-1218 (2010)
[89]Sudipto Das, Divyakant Agrawal, Amr El Abbadi, "G-Store:a scalable data store for transactional multi key access in the cloud". SoCC 2010:163-174
[90]Hoang Tam Vo, Chun Chen, Beng Chin Ooi, "Towards Elastic Transactional Cloud Storage with Range Query Support",PVLDB 3(1):506-517 (2010)
[91]Mike Stonebraker, Daniel Abadi, "C-store:a column oriented dbms", VLDB, Trondheim, Norway,2005. ACM 2005:553-564
[92]Daniel Abadi, "Materialization strategies in a column-oriented dbms", ICDE, Istanbul, Turkey,2007. IEEE 2007:466-475
[93]Daniel Abadi, Samuel Madden, "Integrating compression and execution in column-oriented database systems", SIGMOD, Chicago, Illinois, USA,2006, ACM 2006:671-682
[94]Martin Kersten, Stefan Manegold, "Cracking the database store", CIDR, Asilomar, CA, USA,2005. www.crdrdb.org 2005:213-224
[95]Stratos Idreos, Martin Kersten, Stefan Manegold, "Database cracking", CIDR, Asilomar, CA, USA,2007. www.crdrdb.org 2007:68-78
[96]Jianguo Sun, Tinghua Ai, Chuanyan Zhao, Haowen Yan, "Assessing vegetation degradation in Loess Plateau by using potential vegetation index" IGARSS 2007.July 2007
[97]Min Fang, Yong Liu, Chunpeng Jin, Zhang Ping, "Study on the index system of large-scale non-grid-connected wind power suitable industry", World Non-Grid-Connected Wind Power and Energy Conference,2009. Sept.2009.
[98]Weisshaar, A, "Impedance boundary method of moments for accurate and efficient analysis of planar graded-index optical waveguides", Lightwave Technology, V12, Issue 11, Nov 1994.
[99]Das K K, Arif M, Haider A F M Y, Alam M S, "An interferometric technique for measuring the refractive index profile of a graded index optical fiber", Aerospace and Electronics Conference,1994. NAECON 1994.May 1994
[100]Lueker, George S, "A data structure for orthogonal range queries", Foundations of Computer Science,1978.Oct.1978
[101]Xiangyu Zhang, Jing Ai, Zhongyuan Wang, Jiaheng Lu, Xiaofeng Meng, 'An efficient multi-dimensional index for cloud data management", In:CloudDB, Hong Kong, China,2009. ACM 2009:17-24
[102]Kiyoung Yang, Shahabi.C, "A multilevel distance-based index structure for multivariate time series", Temporal Representation and Reasoning,2005, June 2005
[103]Wattanakitrungroj Niwan, Vanichayobon Sirirut, "Dual Bitmap Index: Space-Time Efficient Bitmap Index for Equality and Membership Queries", Communications and Information Technologies,2006. ISCIT'06.Sept 2006
[104]Weahama W, Vanichayobon S, Manfuekphan J, "Using Data Clustering to Optimize Scatter Bitmap Index for Membership Queries", Computer and Automation Engineering,2009. March 2009
[105]Sainui J, Vanichayobon S, Wattanakitrungroj N, "Optimizing Encoded Bitmap Index Using Frequent Itemsets Mining", Computer and Electrical Engineering,2008. Dec 2008
[106]O'Neil.E, O'Neil.P, Kesheng Wu, "Bitmap Index Design Choices and Their Performance Implications", Database Engineering and Applications Symposium, 2007. IDEAS 2007.Sept 2007
[107]David G. Campbell, Gopal Kakivaya, Nigel Ellis, "Extreme Scale with Full SQL Language Support in Microsoft SQL Azure ", In SIGMOD'10, June 6-10, 2010, Indianapolis, Indiana, USA.
[108]Wolfgang Nejdl, Boris Wolf, Changtao Qu, Stefan Decker, Michael Sintek,"EDUTELLA:A P2P Networking Infrastructure Based on RDF", SIGMOD'10, June 6-10,2010, Indianapolis, Indiana, USA.
[109]Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, Hari Balakrishnan, "Chord:A scalable Peer-to-Peer lookup service for intemet applications", In Proc. SIGCOMM, San Diego, CA, Aug.2001.pp149-160.